L09 Themes

Data Visualization (STAT 302)

Overview

The goal of this lab is to play around with the theme options in ggplot2.

Datasets

We’ll be using the cdc.txt and NU_admission_data.csv datasets.

# load package(s)
library(tidyverse)
library(ggthemes)
library(ggplot2)
library(patchwork)
library(cowplot)
library(showtext)
library(ThemePark)
library(sysfonts)
library(dplyr)

# Enable showtext
showtext_auto()

font_add("ArialBold", "~/System/Library/Fonts/Supplemental/Arial Bold.ttf")

#font_add("CamptonBlack", "~/font/CamptonBlack.otf")

# read in the cdc dataset
cdc <- read_delim(file = "data/cdc.txt", delim = "|") |>
  mutate(
    genhlth = factor(
      genhlth,
      levels = c("excellent", "very good", "good", "fair", "poor"),
      labels = c("Excellent", "Very Good", "Good", "Fair", "Poor")
      )
    )

# read in NU admission data
nu_admission_data <- read_csv("data/NU_admission_data.csv") |> 
  janitor::clean_names()

# set seed
set.seed(2468)

# selecting a random subset of size 100
cdc_small <- cdc |> 
  slice_sample(n = 100)

Exercise 1

Use the cdc_small dataset to explore several pre-set ggthemes. The code below constructs the familiar scatterplot of weight by height and stores it in plot_01. Display plot_01 to observe the default theme. Explore/apply, and display at least 7 other pre-set themes from the ggplot2 or ggthemes package. Don’t worry about making adjustments to the figures under the new themes. Just get a sense of what the themes are doing to the original figure plot_01.

There should be at least 8 plots for this task, plot_01 is pictured below. Use patchwork or cowplot in combination with R yaml chunk options fig-height and fig-width (out-width and fig-align may be useful as well) to setup the 8 plots together in a user friendly arrangement.

# plot
plot_01 <- ggplot(
  data = cdc_small,
  aes(x = height, y = weight)
) +
  geom_point(size = 3, aes(shape = genhlth, color = genhlth)) +
  scale_y_continuous(
    name = "Weight in Pounds",
    limits = c(100, 275),
    breaks = seq(100, 275, 25),
    trans = "log10",
    labels = scales::label_number(
      accuracy = 1,
      suffix = " lbs"
    )
  ) +
  scale_x_continuous(
    name = "Height in Inches",
    limits = c(60, 80),
    breaks = seq(60, 80, 5),
    labels = scales::label_number(accuracy = 1, suffix = " in")
  ) +
  scale_shape_manual(
    name = "Health?",
    labels = c(
      "Excellent", "Very Good",
      "Good", "Fair", "Poor"
    ),
    values = c(17, 19, 15, 9, 4)
  ) +
  scale_color_brewer(
    name = "Health?",
    labels = c(
      "Excellent", "Very Good",
      "Good", "Fair", "Poor"
    ),
    palette = "Set1"
  ) +
  theme(
    legend.position = "inside",
    legend.position.inside = c(1, 0),
    legend.justification = c(1, 0)
  ) +
  labs(title = "CDC BRFSS: Weight by Height")

plot_01

Solution

#comparing 8 themes
plot_01
plot_01 + theme_classic()
plot_01 + theme_economist()
plot_01 + theme_fivethirtyeight()
plot_01 + theme_half_open()
plot_01 + theme_wsj()
plot_01 + theme_avatar()
plot_01 + theme_barbie()
plot_01 + theme_tufte()

Important

Which theme or themes do you particularly like? Why?

Solution

My favorite themes are theme_barbie, it has a ton of pink and barbie fonts which is super interesting considering the difference tech coding and barbie.

Exercise 2

Using plot_01 from Exercise 1 and the theme() function, attempt to construct the ugliest plot possible (example pictured below). Be creative! It should NOT look exactly like the example. Since the goal is to understand a variety of adjustments, you should use a minimum of 10 different manual adjustments within theme().

Solution

# plot
plot_ugly <- ggplot(
  data = cdc_small,
  aes(x = height, y = weight)
) +
  geom_point(size = 0, aes(shape = genhlth, color = genhlth)) +
  scale_y_continuous(
    name = "Weight in Pounds",
    limits = c(100, 275),
    breaks = seq(100, 275, 25),
    trans = "log10",
    labels = scales::label_number(
      accuracy = 1,
      suffix = " lbs"
    )
  ) +
  scale_x_continuous(
    name = "Height in Inches",
    limits = c(60, 80),
    breaks = seq(60, 80, 5),
    labels = scales::label_number(accuracy = 10, suffix = " in")
  ) +
  scale_shape_manual(
    name = "Health?",
    labels = c(
      "Excellent", "Very Good",
      "Good", "Fair", "Poor"
    ),
    values = c(17, 19, 15, 9, 4)
  ) +
  scale_color_brewer(
    name = "Health?",
    labels = c(
      "Excellent", "Very Good",
      "Good", "Fair", "Poor"
    ),
    palette = "Set2"
  ) +
 theme_avatar() +
  theme(
    legend.position = c(0.5, 0.5),
    legend.justification = c(0.5, 0.5),
    plot.title = element_text(hjust = 0.5),
    axis.title = element_text(size = 15),
    axis.title.x = element_text(
      size = 15,
      face = "italic",
      color = "hotpink",
      angle = 20
    ),
    axis.text = element_text(size = 10),
    panel.grid.major = element_line(color = "white", size = 0.5),
    panel.grid.minor = element_line(color = "red", size = 0.2),
    axis.ticks.length.x = unit(.2, "in"),
    legend.background = element_rect(fill = "darkgreen", color = "black")
  ) +
  labs(title = "CDC BRFSS: Weight by Height")

plot_ugly

Exercise 3

We will be making use of your code from Exercise 3 on L07 Layers. Using the NU_admission_data.csv you created two separate plots derived from the single plot depicted in undergraduate-admissions-statistics.pdf. Style these plots so they follow a “Northwestern” theme. You are welcome to display the plots separately OR design a layout that displays both together (likely one stacked above the other).

Check out the following webpages to help create your Northwestern theme:

• Visual Identity
• Color Palettes
• Fonts & Typography — Need to use substitute fonts

Additional requirement:

Use a free non-standard font from google for the title. Pick one that looks similar to a Northwestern font.

Note

I find this blog post to be extremely useful for adding fonts. Important packages for using non-standard fonts are showtext, extrafont, extrafontdb, and sysfonts. The last 3 generally just need to be installed (not loaded per session).

Solution

#data wrangling (move to tidy format for data)
bar_data <- nu_admission_data |> 
  filter(year > 1999) |> 
  select(-contains("_rate")) |> 
  pivot_longer(
    cols = -year, 
    names_to = "category",
    values_to = "value"
  ) |> 
  mutate(
    bar_label = prettyNum(value, big.mark = ",")
  )

#bar plot 
plot_1_bar <- ggplot(data = bar_data, 
       mapping = aes(x = year, y = value, fill = category)
       ) +
  geom_col(position = "identity", width = 0.75) +
  geom_text(
    mapping = aes(label = bar_label),
    color = "white",
    size = 2,
    vjust = 1,
    nudge_y = -300) +
  scale_x_continuous(
    name = "Entering Year",
    breaks = 2000:2020, #seq(2000,2020),
    expand = c(0,0.25)
) + 
  scale_y_continuous(
    name = "Applications",
    limits = c(0,50000),
    breaks = seq(0,50000,5000),
    labels = scales::label_comma(),
    expand = c(0,0)
    )+
  theme_classic() +
  theme(
    legend.justification = c(0.5,1),
    legend.position = c(0.5, 1),
    legend.title = element_text(
      family = "ArialBold",
      color = "#4E2A84",
      size = 10),
    legend.text = element_text(
      family = "ArialBold",
      color = "#4E2A84",
      size = 10
    ),
    legend.direction = "horizontal" ,
    plot.title = element_text(
      hjust = 0.5,
      family = "ArialBold",
      face = "bold",
      size = 20,
      color = "#4E2A84"
    ),
    plot.subtitle = element_text(
      hjust = 0.5,
      family = "ArialBold",
      face = "bold",
      size = 15,
      color = "#4E2A84"
    ),
     axis.title.x = element_text(
      family = "ArialBold",
      color = "#4E2A84",
      face = "bold",
      size = 10
    ),
    axis.title.y = element_text(
      family = "ArialBold",
      color = "#4E2A84",
      face = "bold",
      size = 10
    )
  ) +
  scale_fill_manual(
    name = NULL, 
    limits = c("matriculants", "admitted_students", "applications"),
    labels = c("Matriculants", "Admitted_students", "Applications"),
    values = c("#4E2A84","#836EAA", "#B6ACD1")
  ) +
  labs(
    title = "Northwestern University",
    subtitle = "Undergraduate Admissions 2000-2020"
  )

#data wrangling (move to tidy format for data)
rate_data <- nu_admission_data  |> 
  filter(year > 1999) |> 
  select(year, contains("_rate")) |> 
  pivot_longer(
    cols = -year, 
    names_to = "rate_type",
    values_to = "value"
  )  |> 
 mutate(
   rate_label = str_c(value, "%"),
   label_y = case_when(
   rate_type == "yield_rate" ~ value + 2,
   rate_type == "admission_rate" ~ value + 2,
   )
 )

#data wrangling (move to tidy format for data)
# Assuming rate_data is your data frame
plot_2_rate <- ggplot(data = rate_data, 
       mapping = aes(x = year, y = value, color = rate_type, shape = rate_type)
       ) +
  geom_line() +
  geom_point(mapping = aes(fill = rate_type), color = "white", size = 3, stroke = 1) +
  geom_text(
    mapping = aes(y = label_y, label = rate_label),
    size = 2,
    show.legend = FALSE
  ) +
  scale_x_continuous(
    name = "Entering Year",
    breaks = 2000:2020,
    expand = c(0,0.25)
  ) + 
  scale_y_continuous(
    name = "Rate",
    limits = c(0,60),
    labels = scales::label_percent(scale = 1),
    expand = c(0,0),
    position = "right"
  ) +
scale_color_manual(
    name = NULL, 
    values = c("admission_rate" = "#8E44AD", "yield_rate" = "#D2B4DE"),
    labels = c("Admission Rate", "Yield Rate")
  ) +
 scale_fill_manual(
    name = NULL, 
    values = c("admission_rate" = "#8E44AD", "yield_rate" = "#D2B4DE"),
    labels = c("Admission Rate", "Yield Rate")
  ) +
  scale_shape_manual(
    name = NULL, 
    limits = c("admission_rate", "yield_rate"),
    labels = c("Admission Rate", "Yield Rate"),
    values = c(21, 24)
  ) +
  theme_classic() +
  labs(
    title = "Northwestern University",
    subtitle = "Undergraduate Admissions 2000-2020"
  ) +
  theme(
    legend.text = element_text(
      family = "ArialBold",
      color = "#4E2A84",
      size = 10
    ),
    legend.justification = c(0.5, 1),
    legend.position = c(0.5, 1),
    legend.direction = "horizontal",
    plot.title = element_text(
      hjust = 0.5,
      family = "ArialBold",
      face = "bold",
      size = 20,
      color = "#4E2A84"
    ),
    plot.subtitle = element_text(
      hjust = 0.5,
      family = "ArialBold",
      face = "bold",
      size = 15,
      color = "#4E2A84"
    ),
    axis.title.x = element_text(
      family = "ArialBold",
      color = "#4E2A84",
      face = "bold",
      size = 10
    ),
    axis.title.y = element_text(
      family = "ArialBold",
      color = "#4E2A84",
      face = "bold",
      size = 10
    )
  )


plot_1_bar

plot_2_rate

Challenge

Important

Challenge is optional for all students, but we recommend trying them out!

Using cdc_small dataset, re-create your own version inspired by the plot below.

Must haves:

• Use two non-standard fonts (one for labeling the point and the other for the axes)
• Use at least two colors (one for the added point, another for the rest of the points)
• A curved arrow used to label the point

Using Bilbo Baggins’ responses below to the CDC BRFSS questions, add Bilbo’s data point to a scatterplot of weight by height.

• genhlth - How would you rate your general health? fair
• exerany - Have you exercised in the past month? 1=yes
• hlthplan - Do you have some form of health coverage? 0=no
• smoke100 - Have you smoked at least 100 cigarettes in your life time? 1=yes
• height - height in inches: 46
• weight - weight in pounds: 120
• wtdesire - weight desired in pounds: 120
• age - in years: 45
• gender - m for males and f for females: m

Note

Adding non-standard fonts can be an adventure. I find this blog post to be extremely useful for adding fonts. Important packages for using non-standard fonts are showtext, extrafont, extrafontdb, and sysfonts. The last 3 generally just need to be installed (not loaded per session).

Hint:

• Create a new dataset (maybe call it bilbo or bilbo_baggins) using either data.frame() (base R - example in book) or tibble() (tidyverse - see help documentation for the function). Make sure to use variable names that exactly match cdc’s variable names. We have provided the tidyverse approach.
• Search google fonts to find some free fonts to use (can get free fonts from other locations)

Solution

# build dataset for Bilbo Baggins
bilbo <- tibble(
  genhlth  = "fair",
  exerany  = 1,
  hlthplan = 0,
  smoke100 = 1,
  height   = 46,
  weight   = 120,
  wtdesire = 120,
  age      = 45,
  gender   = "m"
)